Yet, the vast majority of generative models do not directly reflect this compositionality so intrinsic to visual scenes. Rather, most networks, and GANs in particular, aim to generate the entire scene all at once from a single monolithic latent space, through a largely opaque transformation. Consequently, while they excel in producing stunning, strikingly-realistic portrayals of faces, sole centralized objects, or natural scenery, they still struggle to faithfully mimic richer or densely-packed scenes, as those common to everyday contexts, falling short of mastering their nuances and intricacies [1]}, [2]}, [3]}. Likewise, while there is some evidence for property disentanglement within their latent space to independent axes of variation at the global scale [4]}, [5]}, [6]}, [7]}, most existing GANs fail to provide controllability at the level of the individual object or local region. Understanding of the mechanisms by which these models give rise to an output image, and the transparency of their synthesis process, thereby remain rather limited.
